Recently, Graph-based methods have been very popular and aim to model long-range dependencies through graph-structured data. In particular, an image could be regarded as a regular graph-structure data. CRFs [1]} is an effective graph model used as post-processing for image segmentation. Besides, Graph Convolutional Networks (GCN) exhibits the superior performance on the task of reasoning relation. Specifically, Kipf et al. [2]} encodes the graph structure to reason relation between graph nodes for semi-supervised classification. Wang et al. [3]} exploits GCN to capture relations between objects in video recognition tasks. Xu et al. [4]} proposes to use GCN for large-scale object detection, which discovers and incorporates key semantic and spatial relationships for reasoning over each object. Furthermore, Chen et al. [5]} adopts the reasoning power of graph convolutions to reason disjoint and distant regions without extra annotations for semantic segmentation.
